Deep Hypergraph Structure Learning

• URL: http://arxiv.org/abs/2208.12547v1
• Date: Fri, 26 Aug 2022 10:00:11 GMT
• Title: Deep Hypergraph Structure Learning
• Authors: Zizhao Zhang, Yifan Feng, Shihui Ying, Yue Gao
• Abstract summary: Learning on high-order correlation has shown superiority in data representation learning, where hypergraph has been widely used in recent decades. How to generate the hypergraph structure among data is still a challenging task. DeepHGSL is designed to optimize the hypergraph structure for hypergraph-based representation learning.
• Score: 34.972686247703024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning on high-order correlation has shown superiority in data representation learning, where hypergraph has been widely used in recent decades. The performance of hypergraph-based representation learning methods, such as hypergraph neural networks, highly depends on the quality of the hypergraph structure. How to generate the hypergraph structure among data is still a challenging task. Missing and noisy data may lead to "bad connections" in the hypergraph structure and destroy the hypergraph-based representation learning process. Therefore, revealing the high-order structure, i.e., the hypergraph behind the observed data, becomes an urgent but important task. To address this issue, we design a general paradigm of deep hypergraph structure learning, namely DeepHGSL, to optimize the hypergraph structure for hypergraph-based representation learning. Concretely, inspired by the information bottleneck principle for the robustness issue, we first extend it to the hypergraph case, named by the hypergraph information bottleneck (HIB) principle. Then, we apply this principle to guide the hypergraph structure learning, where the HIB is introduced to construct the loss function to minimize the noisy information in the hypergraph structure. The hypergraph structure can be optimized and this process can be regarded as enhancing the correct connections and weakening the wrong connections in the training phase. Therefore, the proposed method benefits to extract more robust representations even on a heavily noisy structure. Finally, we evaluate the model on four benchmark datasets for representation learning. The experimental results on both graph- and hypergraph-structured data demonstrate the effectiveness and robustness of our method compared with other state-of-the-art methods.

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